{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/N/project/suicide_study/pnas_replication/results/log/logit_2.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}18 Aug 2020, 06:19:43
{txt}
{com}. 
. if ("`model'" == "logit"){c -(}
.         use "${c -(}home_dir{c )-}/data/processed/suicide_reg_v1_raw.dta", clear
. {c )-}
{txt}
{com}. 
. if ("`model'" == "mi") {c -(}
.         use "${c -(}home_dir{c )-}/data/processed/suicide_reg_v1_imputed_M10.dta", clear  
. {c )-}
{txt}
{com}. 
. * for now, we use the following simple survey weights 
. if ("`model'" == "mi") {c -(}
.         mi svyset `geo_type' [pw=ObsWgt0] 
. {c )-}
{txt}
{com}. else {c -(}
.         svyset `geo_type' [pw=ObsWgt0]  

      {txt}pweight:{col 16}{res}ObsWgt0
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}county
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}
{com}. {c )-}
{txt}
{com}. 
. * margins for each category
. program margin_interact 
{txt}  1{com}.         args X Y k model
{txt}  2{com}.         sum `X', d 
{txt}  3{com}.         local gap = (`r(max)' - `r(min)') / `k' 
{txt}  4{com}.         if ("`model'" == "logit") {c -(}
{txt}  5{com}.                 margin `Y', at(`X' = (`r(min)' (`gap') `r(max)')) predict(pr)
{txt}  6{com}.         {c )-} 
{txt}  7{com}.         else if ("`model'" == "mi") {c -(}
{txt}  8{com}.                 mimrgns `Y', at(`X' = (`r(min)' (`gap') `r(max)')) predict(pr)
{txt}  9{com}.         {c )-}       
{txt} 10{com}. end 
{txt}
{com}. 
. program mchange_mi
{txt}  1{com}.         args X k model
{txt}  2{com}.         if ("`model'" == "logit") {c -(}
{txt}  3{com}. 
.                 if ("`k'" == "continuous") {c -(}
{txt}  4{com}.                         sum `X' if e(sample), d 
{txt}  5{com}.                         margin, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt}  6{com}.                         mlincom  2 - 1, decimal(7) stat(all)    
{txt}  7{com}.                 {c )-} 
{txt}  8{com}.                 else if ("`k'" == "binary") {c -(}
{txt}  9{com}.                         sum `X' if e(sample), d 
{txt} 10{com}.                         margin, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 11{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 12{com}.                 {c )-} 
{txt} 13{com}.                 else if ("`k'" == "categorical") {c -(}
{txt} 14{com}.                         margin `X' if e(sample)==1 , at() pwcompare predict(pr) 
{txt} 15{com}.                 {c )-}
{txt} 16{com}.         {c )-}
{txt} 17{com}. 
.         else if ("`model'" == "mi") {c -(}
{txt} 18{com}. 
.                 if ("`k'" == "continuous") {c -(}
{txt} 19{com}.                         sum `X' , d 
{txt} 20{com}.                         mimrgns, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 21{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 22{com}.                 {c )-} 
{txt} 23{com}.                 else if ("`k'" == "binary") {c -(}
{txt} 24{com}.                         sum `X' , d 
{txt} 25{com}.                         mimrgns, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 26{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 27{com}.                 {c )-} 
{txt} 28{com}.                 else if ("`k'" == "categorical") {c -(}
{txt} 29{com}.                         mimrgns `X' , at()   pwcompare predict(pr)      
{txt} 30{com}.                 {c )-}
{txt} 31{com}.         {c )-}
{txt} 32{com}. end
{txt}
{com}. 
. * create some dummy codings
. tab Race5, gen(race5_nh)

      {txt}Race5 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}  9,802,895       79.53       79.53
{txt}          2 {c |}{res}  1,330,104       10.79       90.32
{txt}          3 {c |}{res}    260,664        2.11       92.44
{txt}          4 {c |}{res}    250,301        2.03       94.47
{txt}          5 {c |}{res}    681,739        5.53      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res} 12,325,703      100.00
{txt}
{com}.         rename race5_nh1 White_nh 
{res}{txt}
{com}.         rename race5_nh2 Black_nh 
{res}{txt}
{com}.         rename race5_nh3 AIAN_nh 
{res}{txt}
{com}.         rename race5_nh4 AsPI_nh 
{res}{txt}
{com}.         rename race5_nh5 Hispanic
{res}{txt}
{com}. 
. tab MarStat5, gen(ms)

   {txt}MarStat5 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}  6,880,735       55.83       55.83
{txt}          2 {c |}{res}    924,215        7.50       63.32
{txt}          3 {c |}{res}  1,265,861       10.27       73.60
{txt}          4 {c |}{res}    237,203        1.92       75.52
{txt}          5 {c |}{res}  3,017,228       24.48      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res} 12,325,242      100.00
{txt}
{com}.         rename ms1 Marrd5
{res}{txt}
{com}.         rename ms2 Widow5
{res}{txt}
{com}.         rename ms3 Divor5
{res}{txt}
{com}.         rename ms4 Separ5
{res}{txt}
{com}.         rename ms5 NvMar5
{res}{txt}
{com}. 
. tab AgeGrp4, gen(ag) 

    {txt}AgeGrp4 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}  1,930,142       15.66       15.66
{txt}          2 {c |}{res}  3,549,392       28.80       44.46
{txt}          3 {c |}{res}  4,304,660       34.92       79.38
{txt}          4 {c |}{res}  2,541,509       20.62      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res} 12,325,703      100.00
{txt}
{com}.         rename ag1 Age_15_24
{res}{txt}
{com}.         rename ag2 Age_25_44
{res}{txt}
{com}.         rename ag3 Age_45_64
{res}{txt}
{com}.         rename ag4 Age_65_Up
{res}{txt}
{com}. 
. destring St, replace 
{txt}St: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(16685 missing values generated)
{res}{txt}
{com}. 
. * set-up equations
. local religion Rat_GC_ProE Rat_GC_ProM Rat_GC_ProB Rat_GC_Cath Rat_GC_Jew Rat_GC_Oth
{txt}
{com}. local contextual_control Rat_Poverty Rat_Mig_Cum Pop_Den
{txt}
{com}. 
. local religion Rat_GC_ProE Rat_GC_ProM Rat_GC_ProB Rat_GC_Jew Rat_GC_Oth
{txt}
{com}. local contextual_control Rat_Poverty Rat_Mig_Cum Pop_Den
{txt}
{com}. 
. local demographics_raw i.Female c.RAT_Female i.AgeGrp4 c.RAT_AgeGrp4_2 c.RAT_AgeGrp4_3 c.RAT_AgeGrp4_4 i.Race5 c.RAT_Race5_2 c.RAT_Race5_3 c.RAT_Race5_4 c.RAT_Race5_5 i.BornUSA c.RAT_BornUSA i.MarStat5 c.RAT_MarStat5_2 c.RAT_MarStat5_3 c.RAT_MarStat5_4 c.RAT_MarStat5_5
{txt}
{com}. local demographics_same i.Female c.std_same_prop_Sex i.AgeGrp4 c.std_same_prop_AgeGrp4 i.Race5 c.std_same_prop_Race5 i.BornUSA c.std_same_prop_BornUSA i.MarStat5 c.std_same_prop_MarStat5 
{txt}
{com}. local demographics_inter i.Female##c.std_same_prop_Sex i.AgeGrp4##c.std_same_prop_AgeGrp4 i.Race5##c.std_same_prop_Race5 i.BornUSA##c.std_same_prop_BornUSA i.MarStat5##c.std_same_prop_MarStat5
{txt}
{com}. 
. 
. if (`model_version' == 1){c -(}
.         local model_eq i.Year `demographics_raw' `contextual_control' `religion' 
.         local margin_demographics Female RAT_Female AgeGrp4 RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 Race5 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 BornUSA RAT_BornUSA MarStat5 RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 
. {c )-}
{txt}
{com}. if (`model_version' == 2){c -(}
.         local model_eq i.Year `demographics_raw' `contextual_control' `religion' i.UnEmpl c.RAT_UnEmpl i.PhysProb c.RAT_PhysProb
.         local margin_demographics Female RAT_Female AgeGrp4 RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 Race5 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 BornUSA RAT_BornUSA MarStat5 RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 UnEmpl RAT_UnEmpl PhysProb RAT_PhysProb
. {c )-}
{txt}
{com}. if (`model_version' == 3){c -(}
.         local model_eq i.Year `demographics_same' `contextual_control' `religion' 
.         local margin_demographics Female std_same_prop_Sex AgeGrp4 std_same_prop_AgeGrp4 Race5 std_same_prop_Race5 BornUSA std_same_prop_BornUSA MarStat5 std_same_prop_MarStat5 
. {c )-}
{txt}
{com}. if (`model_version' == 4){c -(}
.         local model_eq i.Year `demographics_same' `contextual_control' `religion' i.UnEmpl c.std_same_prop_UnEmpl i.PhysProb c.std_same_prop_PhysProb
.         local margin_demographics Female std_same_prop_Sex AgeGrp4 std_same_prop_AgeGrp4 Race5 std_same_prop_Race5 BornUSA std_same_prop_BornUSA MarStat5 std_same_prop_MarStat5 UnEmpl std_same_prop_UnEmpl PhysProb std_same_prop_PhysProb
. {c )-}
{txt}
{com}. if (`model_version' == 5){c -(}
.         local model_eq i.Year `demographics_inter' `contextual_control' `religion' 
. {c )-}
{txt}
{com}. if (`model_version' == 6){c -(}
.         local model_eq i.Year `demographics_inter' `contextual_control' `religion' i.UnEmpl##c.std_same_prop_UnEmpl i.PhysProb##c.std_same_prop_PhysProb
. {c )-}       
{txt}
{com}. * test how long it would take.
. * mi estimate: svy: mean Suic 
. * local demographics i.Female c.RAT_Female i.AgeGrp4 c.RAT_AgeGrp4_2 c.RAT_AgeGrp4_3 c.RAT_AgeGrp4_4 i.Race5 c.RAT_Race5_2 c.RAT_Race5_3 c.RAT_Race5_4 c.RAT_Race5_5 i.BornUSA c.RAT_BornUSA i.MarStat5 c.RAT_MarStat5_2 c.RAT_MarStat5_3 c.RAT_MarStat5_4 c.RAT_MarStat5_5
. * mi estimate: svy: logit Suic i.St `demographics' UnEmpl RAT_UnEmpl PhysProb RAT_PhysProb
. 
. * main effects : margins
. if ("`model'" == "mi"){c -(}
. 
.         mi estimate: svy: logit Suic i.St `model_eq', or 
.         estimates store m1 
. 
.         if (`model_version' <= 4){c -(}
.                 estimates restore m1
.                 mchange_mi Female "binary" "mi"
.                 estimates restore m1
.                 mchange_mi AgeGrp4 "categorical" "mi"
.                 estimates restore m1
.                 mchange_mi Race5 "categorical" "mi"
.                 estimates restore m1
.                 mchange_mi BornUSA "binary" "mi"
.                 estimates restore m1
.                 mchange_mi MarStat5 "categorical" "mi"
.                 
.                 if (`model_version' == 1 | `model_version' == 2) {c -(}
.                         estimates restore m1
.                         mchange_mi RAT_Female "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_5 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_BornUSA "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_5 "continuous" "mi"
.                 {c )-}
.                 if (`model_version' == 3 | `model_version' == 4) {c -(}
.                         estimates restore m1
.                         mchange_mi std_same_prop_Sex "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_AgeGrp4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_Race5 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_BornUSA "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_MarStat5 "continuous" "mi"
.                 {c )-}
.         
.                 if (`model_version' == 2 | `model_version' == 4){c -(}
.                         estimates restore m1
.                         mchange_mi UnEmpl "categorical" "mi"
.                         estimates restore m1
.                         mchange_mi PhysProb "categorical" "mi"
.                 {c )-}
.         
.                 if (`model_version' == 2){c -(}
.                         estimates restore m1
.                         mchange_mi RAT_UnEmpl "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_PhysProb "continuous" "mi"
.                 {c )-}
.                 if (`model_version' == 4){c -(}
.                         estimates restore m1
.                         mchange_mi std_same_prop_UnEmpl "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_PhysProb "continuous" "mi"
.                 {c )-}               
.         {c )-}
. {c )-}
{txt}
{com}. 
. if ("`model'" == "logit"){c -(}
.         svy: logit Suic i.St `model_eq', or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 47}Number of obs{col 65}= {res}  11,587,123
{txt}{col 1}Number of PSUs{col 20}= {res}      918{txt}{col 47}Population size{col 65}={res}  411,228,041
{txt}{col 47}Design df{col 65}= {res}         917
{txt}{col 47}F({res}  59{txt},{res}    859{txt}){col 65}= {res}      429.66
{txt}{col 47}Prob > F{col 65}= {res}      0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        Suic{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}St {c |}
{space 10}8  {c |}{col 14}{res}{space 2} 1.077604{col 26}{space 2} .2234283{col 37}{space 1}    0.36{col 46}{space 3}0.719{col 54}{space 4} .7173641{col 67}{space 3} 1.618747
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .2208837{col 26}{space 2} .0479751{col 37}{space 1}   -6.95{col 46}{space 3}0.000{col 54}{space 4} .1442254{col 67}{space 3} .3382872
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .6501698{col 26}{space 2} .1383835{col 37}{space 1}   -2.02{col 46}{space 3}0.043{col 54}{space 4} .4281696{col 67}{space 3}  .987274
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .4460774{col 26}{space 2} .1006534{col 37}{space 1}   -3.58{col 46}{space 3}0.000{col 54}{space 4} .2864774{col 67}{space 3} .6945924
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .1527598{col 26}{space 2} .0379757{col 37}{space 1}   -7.56{col 46}{space 3}0.000{col 54}{space 4} .0937827{col 67}{space 3} .2488258
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .4350335{col 26}{space 2} .1058418{col 37}{space 1}   -3.42{col 46}{space 3}0.001{col 54}{space 4} .2698705{col 67}{space 3} .7012778
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .9461404{col 26}{space 2} .2775102{col 37}{space 1}   -0.19{col 46}{space 3}0.850{col 54}{space 4} .5320614{col 67}{space 3} 1.682478
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .2934794{col 26}{space 2} .0651023{col 37}{space 1}   -5.53{col 46}{space 3}0.000{col 54}{space 4} .1898921{col 67}{space 3} .4535743
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .3003177{col 26}{space 2} .0637121{col 37}{space 1}   -5.67{col 46}{space 3}0.000{col 54}{space 4} .1980436{col 67}{space 3} .4554083
{txt}{space 9}41  {c |}{col 14}{res}{space 2}  .875507{col 26}{space 2} .1617747{col 37}{space 1}   -0.72{col 46}{space 3}0.472{col 54}{space 4} .6092115{col 67}{space 3} 1.258204
{txt}{space 9}44  {c |}{col 14}{res}{space 2}  .306638{col 26}{space 2} .0814634{col 37}{space 1}   -4.45{col 46}{space 3}0.000{col 54}{space 4} .1820504{col 67}{space 3} .5164882
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .3423966{col 26}{space 2} .0859443{col 37}{space 1}   -4.27{col 46}{space 3}0.000{col 54}{space 4} .2092129{col 67}{space 3} .5603641
{txt}{space 9}49  {c |}{col 14}{res}{space 2}  1.82514{col 26}{space 2} .5948705{col 37}{space 1}    1.85{col 46}{space 3}0.065{col 54}{space 4} .9627119{col 67}{space 3}  3.46016
{txt}{space 9}51  {c |}{col 14}{res}{space 2}  1.46635{col 26}{space 2} .2988711{col 37}{space 1}    1.88{col 46}{space 3}0.061{col 54}{space 4} .9829169{col 67}{space 3} 2.187554
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .8029712{col 26}{space 2} .1691739{col 37}{space 1}   -1.04{col 46}{space 3}0.298{col 54}{space 4} .5310406{col 67}{space 3}  1.21415
{txt}{space 12} {c |}
{space 8}Year {c |}
{space 7}2006  {c |}{col 14}{res}{space 2} .9703452{col 26}{space 2} .0328572{col 37}{space 1}   -0.89{col 46}{space 3}0.374{col 54}{space 4} .9079572{col 67}{space 3}  1.03702
{txt}{space 7}2007  {c |}{col 14}{res}{space 2} 1.063871{col 26}{space 2} .0343535{col 37}{space 1}    1.92{col 46}{space 3}0.056{col 54}{space 4} .9985419{col 67}{space 3} 1.133474
{txt}{space 7}2008  {c |}{col 14}{res}{space 2}   1.1497{col 26}{space 2} .0377218{col 37}{space 1}    4.25{col 46}{space 3}0.000{col 54}{space 4} 1.078002{col 67}{space 3} 1.226167
{txt}{space 7}2009  {c |}{col 14}{res}{space 2}  1.11177{col 26}{space 2} .0408263{col 37}{space 1}    2.89{col 46}{space 3}0.004{col 54}{space 4} 1.034465{col 67}{space 3} 1.194852
{txt}{space 7}2010  {c |}{col 14}{res}{space 2} 1.056108{col 26}{space 2} .0384939{col 37}{space 1}    1.50{col 46}{space 3}0.135{col 54}{space 4} .9831999{col 67}{space 3} 1.134422
{txt}{space 7}2011  {c |}{col 14}{res}{space 2} 1.119469{col 26}{space 2} .0443014{col 37}{space 1}    2.85{col 46}{space 3}0.004{col 54}{space 4} 1.035816{col 67}{space 3} 1.209878
{txt}{space 12} {c |}
{space 4}1.Female {c |}{col 14}{res}{space 2} .2403752{col 26}{space 2} .0065163{col 37}{space 1}  -52.59{col 46}{space 3}0.000{col 54}{space 4} .2279208{col 67}{space 3} .2535101
{txt}{space 2}RAT_Female {c |}{col 14}{res}{space 2} .9813527{col 26}{space 2} .0176086{col 37}{space 1}   -1.05{col 46}{space 3}0.294{col 54}{space 4} .9473962{col 67}{space 3} 1.016526
{txt}{space 12} {c |}
{space 5}AgeGrp4 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} 1.877155{col 26}{space 2} .0544639{col 37}{space 1}   21.71{col 46}{space 3}0.000{col 54}{space 4} 1.773252{col 67}{space 3} 1.987145
{txt}{space 10}3  {c |}{col 14}{res}{space 2}  2.13792{col 26}{space 2} .0737829{col 37}{space 1}   22.02{col 46}{space 3}0.000{col 54}{space 4} 1.997912{col 67}{space 3} 2.287739
{txt}{space 10}4  {c |}{col 14}{res}{space 2} 1.757308{col 26}{space 2} .0795881{col 37}{space 1}   12.45{col 46}{space 3}0.000{col 54}{space 4} 1.607853{col 67}{space 3} 1.920656
{txt}{space 12} {c |}
RAT_AgeGrp~2 {c |}{col 14}{res}{space 2} 1.002649{col 26}{space 2} .0123064{col 37}{space 1}    0.22{col 46}{space 3}0.829{col 54}{space 4} .9787856{col 67}{space 3} 1.027094
{txt}RAT_AgeGrp~3 {c |}{col 14}{res}{space 2} .9883809{col 26}{space 2} .0142568{col 37}{space 1}   -0.81{col 46}{space 3}0.418{col 54}{space 4} .9607934{col 67}{space 3}  1.01676
{txt}RAT_AgeGrp~4 {c |}{col 14}{res}{space 2} 1.046502{col 26}{space 2} .0191091{col 37}{space 1}    2.49{col 46}{space 3}0.013{col 54}{space 4} 1.009663{col 67}{space 3} 1.084684
{txt}{space 12} {c |}
{space 7}Race5 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .3199558{col 26}{space 2} .0124821{col 37}{space 1}  -29.21{col 46}{space 3}0.000{col 54}{space 4} .2963733{col 67}{space 3} .3454146
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .8813458{col 26}{space 2} .0927816{col 37}{space 1}   -1.20{col 46}{space 3}0.231{col 54}{space 4} .7168357{col 67}{space 3}  1.08361
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .6918342{col 26}{space 2} .0398026{col 37}{space 1}   -6.40{col 46}{space 3}0.000{col 54}{space 4}  .617968{col 67}{space 3} .7745296
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .4264045{col 26}{space 2} .0204264{col 37}{space 1}  -17.79{col 46}{space 3}0.000{col 54}{space 4} .3881432{col 67}{space 3} .4684373
{txt}{space 12} {c |}
{space 1}RAT_Race5_2 {c |}{col 14}{res}{space 2} 1.003202{col 26}{space 2} .0038306{col 37}{space 1}    0.84{col 46}{space 3}0.403{col 54}{space 4} .9957129{col 67}{space 3} 1.010748
{txt}{space 1}RAT_Race5_3 {c |}{col 14}{res}{space 2} 1.007061{col 26}{space 2} .0054258{col 37}{space 1}    1.31{col 46}{space 3}0.192{col 54}{space 4} .9964683{col 67}{space 3} 1.017766
{txt}{space 1}RAT_Race5_4 {c |}{col 14}{res}{space 2} .9807262{col 26}{space 2} .0154184{col 37}{space 1}   -1.24{col 46}{space 3}0.216{col 54}{space 4} .9509288{col 67}{space 3} 1.011457
{txt}{space 1}RAT_Race5_5 {c |}{col 14}{res}{space 2}  .997107{col 26}{space 2} .0066426{col 37}{space 1}   -0.43{col 46}{space 3}0.664{col 54}{space 4} .9841555{col 67}{space 3} 1.010229
{txt}{space 3}1.BornUSA {c |}{col 14}{res}{space 2} 1.422987{col 26}{space 2} .0627138{col 37}{space 1}    8.00{col 46}{space 3}0.000{col 54}{space 4} 1.305081{col 67}{space 3} 1.551546
{txt}{space 1}RAT_BornUSA {c |}{col 14}{res}{space 2} .9882089{col 26}{space 2} .0081761{col 37}{space 1}   -1.43{col 46}{space 3}0.152{col 54}{space 4} .9722925{col 67}{space 3} 1.004386
{txt}{space 12} {c |}
{space 4}MarStat5 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} 1.988384{col 26}{space 2} .0617396{col 37}{space 1}   22.14{col 46}{space 3}0.000{col 54}{space 4} 1.870835{col 67}{space 3} 2.113319
{txt}{space 10}3  {c |}{col 14}{res}{space 2}   2.6193{col 26}{space 2} .0517362{col 37}{space 1}   48.75{col 46}{space 3}0.000{col 54}{space 4} 2.519707{col 67}{space 3} 2.722829
{txt}{space 10}4  {c |}{col 14}{res}{space 2} 1.259336{col 26}{space 2} .1294911{col 37}{space 1}    2.24{col 46}{space 3}0.025{col 54}{space 4} 1.029204{col 67}{space 3} 1.540927
{txt}{space 10}5  {c |}{col 14}{res}{space 2} 1.877427{col 26}{space 2} .0463317{col 37}{space 1}   25.52{col 46}{space 3}0.000{col 54}{space 4} 1.788666{col 67}{space 3} 1.970594
{txt}{space 12} {c |}
RAT_MarSta~2 {c |}{col 14}{res}{space 2} .9103666{col 26}{space 2} .0426742{col 37}{space 1}   -2.00{col 46}{space 3}0.045{col 54}{space 4} .8303531{col 67}{space 3} .9980904
{txt}RAT_MarSta~3 {c |}{col 14}{res}{space 2} 1.042638{col 26}{space 2} .0171542{col 37}{space 1}    2.54{col 46}{space 3}0.011{col 54}{space 4}  1.00951{col 67}{space 3} 1.076854
{txt}RAT_MarSta~4 {c |}{col 14}{res}{space 2} .9774941{col 26}{space 2} .0593347{col 37}{space 1}   -0.38{col 46}{space 3}0.708{col 54}{space 4} .8677153{col 67}{space 3} 1.101162
{txt}RAT_MarSta~5 {c |}{col 14}{res}{space 2} 1.003341{col 26}{space 2} .0089083{col 37}{space 1}    0.38{col 46}{space 3}0.707{col 54}{space 4} .9860095{col 67}{space 3} 1.020977
{txt}{space 1}Rat_Poverty {c |}{col 14}{res}{space 2} .9956041{col 26}{space 2} .0049255{col 37}{space 1}   -0.89{col 46}{space 3}0.373{col 54}{space 4} .9859844{col 67}{space 3} 1.005318
{txt}{space 1}Rat_Mig_Cum {c |}{col 14}{res}{space 2} .5024971{col 26}{space 2} .5997573{col 37}{space 1}   -0.58{col 46}{space 3}0.564{col 54}{space 4} .0482876{col 67}{space 3} 5.229151
{txt}{space 5}Pop_Den {c |}{col 14}{res}{space 2} .9999676{col 26}{space 2} .0000179{col 37}{space 1}   -1.81{col 46}{space 3}0.071{col 54}{space 4} .9999324{col 67}{space 3} 1.000003
{txt}{space 1}Rat_GC_ProE {c |}{col 14}{res}{space 2} .9999706{col 26}{space 2} .0000252{col 37}{space 1}   -1.16{col 46}{space 3}0.244{col 54}{space 4} .9999211{col 67}{space 3}  1.00002
{txt}{space 1}Rat_GC_ProM {c |}{col 14}{res}{space 2} 1.000037{col 26}{space 2} .0000235{col 37}{space 1}    1.58{col 46}{space 3}0.115{col 54}{space 4}  .999991{col 67}{space 3} 1.000083
{txt}{space 1}Rat_GC_ProB {c |}{col 14}{res}{space 2} .9999587{col 26}{space 2} .0001424{col 37}{space 1}   -0.29{col 46}{space 3}0.772{col 54}{space 4} .9996792{col 67}{space 3} 1.000238
{txt}{space 2}Rat_GC_Jew {c |}{col 14}{res}{space 2}  1.00007{col 26}{space 2} .0000903{col 37}{space 1}    0.77{col 46}{space 3}0.440{col 54}{space 4} .9998926{col 67}{space 3} 1.000247
{txt}{space 2}Rat_GC_Oth {c |}{col 14}{res}{space 2}  .999932{col 26}{space 2} .0000405{col 37}{space 1}   -1.68{col 46}{space 3}0.094{col 54}{space 4} .9998525{col 67}{space 3} 1.000012
{txt}{space 4}1.UnEmpl {c |}{col 14}{res}{space 2} 6.046043{col 26}{space 2} .3090222{col 37}{space 1}   35.21{col 46}{space 3}0.000{col 54}{space 4} 5.468995{col 67}{space 3} 6.683976
{txt}{space 2}RAT_UnEmpl {c |}{col 14}{res}{space 2} 1.070753{col 26}{space 2} .0300467{col 37}{space 1}    2.44{col 46}{space 3}0.015{col 54}{space 4} 1.013379{col 67}{space 3} 1.131375
{txt}{space 2}1.PhysProb {c |}{col 14}{res}{space 2} 1.793501{col 26}{space 2} .0800535{col 37}{space 1}   13.09{col 46}{space 3}0.000{col 54}{space 4} 1.643076{col 67}{space 3} 1.957697
{txt}RAT_PhysProb {c |}{col 14}{res}{space 2}  .991186{col 26}{space 2} .0111131{col 37}{space 1}   -0.79{col 46}{space 3}0.430{col 54}{space 4} .9696141{col 67}{space 3} 1.013238
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0002237{col 26}{space 2} .0004153{col 37}{space 1}   -4.53{col 46}{space 3}0.000{col 54}{space 4} 5.85e-06{col 67}{space 3} .0085505
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}
{com}.         estimates store m1 
. 
.         if (`model_version' <= 4){c -(}
.                 mchange `margin_demographics', amount(all) delta(100) statistics(all) decimals(7)

{res}svy logit: Changes in Pr(y) | Number of obs = 11525152

{txt}Expression: Pr(Suic), predict(pr)
{res}
{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Change}{space 1}{space 1}{ralign 9:p-value}{space 1}{space 1}{ralign 9:LL}{space 1}{space 1}{ralign 9:UL}{space 1}{space 1}{ralign 9:z-value}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{res:{lalign 13:Female}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000859}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000895}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000822}}}{space 1}{space 1}{ralign 9:{res:{sf:-4.60e+01}}}{space 1}
{space 0}{res:{lalign 13:RAT Female}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000034}}}{space 1}{space 1}{ralign 9:{res:{sf:0.5909107}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000156}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000089}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.5377057}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000013}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2899758}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000037}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.06e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000585}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0020071}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000956}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000214}}}{space 1}{space 1}{ralign 9:{res:{sf:-3.10e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000128}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3047624}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000373}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000117}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.03e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000013}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2945173}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000037}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.05e+00}}}{space 1}
{space 0}{res:{lalign 13:AgeGrp4}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000347}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000318}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000376}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.33e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000450}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000414}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000487}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.42e+01}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000300}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000252}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000347}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.25e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000103}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000072}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000134}}}{space 1}{space 1}{ralign 9:{res:{sf:6.5257290}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000047}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0407167}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000093}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000002}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.05e+00}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000151}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000188}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000113}}}{space 1}{space 1}{ralign 9:{res:{sf:-7.85e+00}}}{space 1}
{space 0}{res:{lalign 13:RAT AgeGrp~2}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000002}}}{space 1}{space 1}{ralign 9:{res:{sf:0.8132483}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000016}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2363057}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000002}}}{space 1}{space 1}{ralign 9:{res:{sf:0.8296759}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000015}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000018}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2151786}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000209}}}{space 1}{space 1}{ralign 9:{res:{sf:0.8498622}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001956}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002374}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1893477}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000047}}}{space 1}{space 1}{ralign 9:{res:{sf:0.8304527}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000387}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000482}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2141821}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000002}}}{space 1}{space 1}{ralign 9:{res:{sf:0.8294543}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000015}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000018}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2154630}}}{space 1}
{space 0}{res:{lalign 13:RAT AgeGrp~3}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:0.5545133}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000050}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000027}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.5912281}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:0.4167940}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000027}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.8123633}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000476}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1269442}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001086}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000135}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.53e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000193}}}{space 1}{space 1}{ralign 9:{res:{sf:0.4205016}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000662}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000277}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.8059136}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:0.4195047}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000028}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.8076445}}}{space 1}
{space 0}{res:{lalign 13:RAT AgeGrp~4}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000016}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000020}}}{space 1}{space 1}{ralign 9:{res:{sf:7.4899490}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000032}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0157227}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000058}}}{space 1}{space 1}{ralign 9:{res:{sf:2.4197970}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0062291}}}{space 1}{space 1}{ralign 9:{res:{sf:0.5775025}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0157096}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0281679}}}{space 1}{space 1}{ralign 9:{res:{sf:0.5572355}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000703}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0390345}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000035}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001371}}}{space 1}{space 1}{ralign 9:{res:{sf:2.0667866}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000031}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0135111}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000056}}}{space 1}{space 1}{ralign 9:{res:{sf:2.4747957}}}{space 1}
{space 0}{res:{lalign 13:Race5}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000557}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000585}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000528}}}{space 1}{space 1}{ralign 9:{res:{sf:-3.79e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000097}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2013549}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000246}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000052}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.28e+00}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000252}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000317}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000187}}}{space 1}{space 1}{ralign 9:{res:{sf:-7.59e+00}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000469}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000507}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000432}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.45e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000459}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000309}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000610}}}{space 1}{space 1}{ralign 9:{res:{sf:6.0074690}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000304}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000241}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000367}}}{space 1}{space 1}{ralign 9:{res:{sf:9.4882287}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000087}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000053}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000121}}}{space 1}{space 1}{ralign 9:{res:{sf:5.0574900}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000155}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0636328}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000319}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.86e+00}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000372}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000017}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000524}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000220}}}{space 1}{space 1}{ralign 9:{res:{sf:-4.81e+00}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000217}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000284}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000151}}}{space 1}{space 1}{ralign 9:{res:{sf:-6.40e+00}}}{space 1}
{space 0}{res:{lalign 13:RAT Race5 2}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000002}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3881118}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000003}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000007}}}{space 1}{space 1}{ralign 9:{res:{sf:0.8634574}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000002}}}{space 1}{space 1}{ralign 9:{res:{sf:0.4039417}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000003}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000007}}}{space 1}{space 1}{ralign 9:{res:{sf:0.8349885}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000260}}}{space 1}{space 1}{ralign 9:{res:{sf:0.4741499}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000452}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000972}}}{space 1}{space 1}{ralign 9:{res:{sf:0.7160384}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000152}}}{space 1}{space 1}{ralign 9:{res:{sf:0.4362982}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000232}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000536}}}{space 1}{space 1}{ralign 9:{res:{sf:0.7788005}}}{space 1}
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{space 0}{res:{lalign 13:BornUSA}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
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{space 0}{res:{lalign 13:UnEmpl}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
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{space 0}{res:{lalign 13:RAT UnEmpl}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
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{space 0}{res:{lalign 13:PhysProb}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
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{space 0}{res:{lalign 13:RAT PhysProb}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
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{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:0.4286141}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000021}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.7919166}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000405}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2056984}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001033}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000223}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.27e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000161}}}{space 1}{space 1}{ralign 9:{res:{sf:0.4067497}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000540}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000219}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.8300091}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:0.4306522}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000021}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.7884245}}}{space 1}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Std Err}{space 1}{space 1}{ralign 9:From}{space 1}{space 1}{ralign 9:To}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{res:{lalign 13:Female}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000019}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001130}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000272}}}{space 1}
{space 0}{res:{lalign 13:RAT Female}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000063}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001805}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001771}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000677}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000189}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000105}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000125}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000770}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000642}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:AgeGrp4}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000015}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000396}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000743}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000019}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000396}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000846}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000024}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000396}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000695}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000016}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000743}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000846}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000023}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000743}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000695}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000019}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000846}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000695}}}{space 1}
{space 0}{res:{lalign 13:RAT AgeGrp~2}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000007}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000631}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000633}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000692}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001103}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000899}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000221}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000671}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000718}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:RAT AgeGrp~3}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000020}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001002}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000990}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000010}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000682}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000311}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000214}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000239}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000790}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000597}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000010}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:RAT AgeGrp~4}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000002}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000339}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000355}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000013}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000722}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0111787}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0062981}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000340}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000505}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001209}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000013}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:Race5}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000015}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000819}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000262}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000076}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000819}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000721}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000033}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000819}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000566}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000019}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000819}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000349}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000076}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000262}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000721}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000032}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000262}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000566}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000017}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000262}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000349}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000084}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000721}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000566}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000077}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000721}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000349}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000034}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000566}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000349}}}{space 1}
{space 0}{res:{lalign 13:RAT Race5 2}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000002}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000667}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000669}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000003}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000692}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000363}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000950}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000196}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000668}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000820}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000003}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:RAT Race5 3}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000684}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000689}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000695}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000751}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001394}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000352}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000684}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001060}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:RAT Race5 4}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000738}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000724}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000677}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000153}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000099}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000163}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000736}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000509}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:RAT Race5 5}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000709}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000707}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000688}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000344}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000517}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000269}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000708}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000583}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:BornUSA}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000023}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000498}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000708}}}{space 1}
{space 0}{res:{lalign 13:RAT BornUSA}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000033}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001979}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001955}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000682}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000174}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000211}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000425}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001113}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000609}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:MarStat5}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000028}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000467}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000929}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000023}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000467}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001223}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000060}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000467}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000588}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000019}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000467}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000877}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000033}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000929}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001223}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000066}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000929}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000588}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000034}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000929}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000877}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000062}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001223}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000588}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000024}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001223}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000877}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000057}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000588}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000877}}}{space 1}
{space 0}{res:{lalign 13:RAT MarSta~2}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000084}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001221}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001111}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000029}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000628}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000253}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000966}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000475}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000032}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:RAT MarSta~3}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000442}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000461}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000719}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0070834}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0043912}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000114}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000563}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000846}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:RAT MarSta~4}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000045}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000724}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000707}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000041}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000675}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000431}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000071}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000189}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000714}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000641}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000042}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:RAT MarSta~5}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000625}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000628}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000692}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000854}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000963}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000201}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000670}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000744}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:UnEmpl}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000129}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000551}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0003330}}}{space 1}
{space 0}{res:{lalign 13:RAT UnEmpl}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000010}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000494}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000529}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000021}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000739}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.1246801}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0521844}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000195}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000589}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001007}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000019}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:PhysProb}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000047}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000620}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001111}}}{space 1}
{space 0}{res:{lalign 13:RAT PhysProb}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000010}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000780}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000773}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000684}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000320}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000285}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000193}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000738}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000577}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{res}
{txt}{p 0 0 2}Average predictions{p_end}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:0}{space 1}{space 1}{ralign 9:1}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:Pr(y|base)}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.9999310}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}

{col 1}1: Delta equals 100.
{com}.         {c )-}
. 
. {c )-}
{txt}
{com}. 
. if (`model_version' == 5 | `model_version' == 6) {c -(}
.         estimates restore m1
.         margin_interact std_same_prop_Sex Female 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_AgeGrp4 AgeGrp4 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_Race5 Race5 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_BornUSA BornUSA 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_MarStat5 MarStat5 5 `model'
. 
.         if (`model_version' == 6) {c -(}
.                 estimates restore m1
.                 margin_interact std_same_prop_UnEmpl UnEmpl 5 `model'
.                 estimates restore m1
.                 margin_interact std_same_prop_PhysProb PhysProb 5 `model'
.         {c )-}
. 
. {c )-}
{txt}
{com}. 
. 
. log close 
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/N/project/suicide_study/pnas_replication/results/log/logit_2.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}18 Aug 2020, 08:43:43
{txt}{.-}
{smcl}
{txt}{sf}{ul off}